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Publisher's Summary

Do you want to master data science?
Understand Markov models and learn the real world application to accurately predict future events.
Extend your knowledge of machine learning, python programming, and algorithms.
What you'll learn:

Mathematics behind Markov algorithms
Three main problems of Markov models and how to overcome them
Uses and applications for machine learning
Python programming
Speech recognition
Weather reporting
The Markov rule and Markov's model
Fundamental axioms of statistics and probability
Artificial intelligence
Bayesian inference
Important tools used with HMM
And much, much more!
The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov models in Python programming.
This audiobook offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else?
Get equipped with the knowledge you need to advance yourself today.
©2017 Healthy Pragmatic Solutions Inc. (P)2017 Healthy Pragmatic Solutions Inc.
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Customer Reviews

Most Helpful
1 out of 5 stars
By Fred on 03-03-18

superficial and off topic

The majority of this book tries to introduce generic data science topics to computer science novices, most of which are irrelevant to Markov models.
the couple chapters that do cover Markov models are superficial.
you'd be better off finding a couple good blog posts than getting this book.

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5 out of 5 stars
By nancy on 10-23-17

4 stars

I like the book. But I gave 4 stars for this one I bought because it contains too many types. I have gotten some background in probability so I wished I could guess what their right words should be.

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Customer Reviews

Most Helpful
5 out of 5 stars
By Marjorie on 11-22-17


This is a very interesting book about Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian networks. In it I listened a lot of interesting and informative about Foundations of Markov Models, Case Study: Google PageRank and Inference Tasks

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5 out of 5 stars
By Madeleine B Howard on 11-22-17

easy to understand

The simplest Markov model is the Markov chain. It models the state of a system with a random variable that changes through time.

By listening this book, I was able to learn the foundation of Markov Models, Markov chains, hidden Markov models and many useful information. Great read. easy to understand.

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